THE EFFECT OF SARBANES-OXLEY ON EARNINGS MANAGEMENT BEHAVIOR

by

GEORGE WILSON

(Under the Direction of Benjamin Ayers)

ABSTRACT

This paper investigates the impact of Sarbanes-Oxley (SOX)

on managers’ earnings management choices (i.e., accrual

management and real earnings management). Specifically, I

investigate whether firms reduce their use of accrual management

and increase their use of real earnings management post-SOX.

SOX likely increases the cost of engaging in accrual management

because of increased legal liability for executives, greater

auditor independelce, and increased public awareness of

aggressive accounting treatments. An increased cost of aacrual

management is likely to lead managers to use other methodq to

manage earnings (e.g., real earnings management through sales

manipulation, reduction of discretionary expenditures, and

overproduction). Consistent with this expectation, I find an

increased association between certain types of real earnings

management (overproduction and sales manipulation) and the

propensity to beat the profit and earnings change benchmarks.

Results also indicate that the associations between abnormal

accruals and beating the profit and earnings change benchmarks

dm not change post-SOX. Contrary to recent evidence suggesting

a decline, on average, in accruals management post-Sox, these

results suggest there was no significant decline in accruals

management for firms with strong incentives to manage earnings

(i.e., firms with earnings close to earnings benchmarks).

INDEX WORDS: Earnings Management, Real Earnings Management,

Production Cost Management, Discretionary

Expenses Management, Sarbanes-Oxley Act of 2001,

Earnings Benchmarks

THE EFFECT OF SARBANES-OXLEY ON EARNINGS MANAGEMENT

BEHAVIOR

by

GEORGE WILSON

B.B.A., The University of Memphis, 2001

A Dissertation Submitted to the Graduate Faculty of The

University of Georgia In Partial Fulfillment of the Requirements

for the Degree

DOCTOR OF PHILOSOPHY

ATHENS, GEORGIA

2006

© 2006

George Wilson

All Rights Reserved

THE EFFECT OF SARBANES-OXLEY ON EARNINGS MANAGEMENT

BEHAVIOR

by

GEORGE WILSON

Major Professor: Benjamin Ayers

Committee: Scott Atkinson Stephen Baginski Jennifer Gaver

Electronic Version Approved: Maureen Grasso Dean of the Graduate School The University of Georgia August 2006

ACKNOWLEDGEMENTS

I wish to express my appreciation for everyone who has

helped me in pursuit of this degree. I must first thank my wife

Heather for her tremendous support, encouragement, and

understanding throughout my entire educational process. I must

also thank my children, Sterling, Savannah, and Eli, as well as

my brother and sister for their encouragement.

I also would like to thank all of the members of my

dissertation committee, Ben Ayers, Jenny Gaver, Ken Gaver, Steve

Baginski, and Scott Atkinson for their time spent in reviewing

this work and offering comments to help improve the paper.

Their input has been invaluable in helping to craft my

understanding of earnings management and the research process.

I wish to especially thank Ben Ayers for his long-suffering

patience and keen insights. His guidance and example of

professionalism will continue to benefit me throughout my

career.

Finally, I would like to thank the entire faculty of the

J.M. Tull School of Accounting. Each member of the faculty has

taken time to assist me whenever I have asked or whenever they

saw an opportunity to help. The lessons they have taught me go

far beyond the classrooms.

vii

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS...............................................vi

LIST OF TABLES.................................................ix

CHAPTER

1 INTRODUCTION.............................................1

1.1. Statement of Issues...............................1

1.2. Summary of Research Methods and Results...........4

1.3. Contributions.....................................6

1.4. Organization of the Dissertation..................7

2 LITERATURE REVIEW........................................8

2.1. Empirical Definitions of Earnings Management......8

2.2. Incentives to Beat Earnings Benchmarks...........10

2.3. Earnings Management to Meet/Beat Earnings

Benchmarks.......................................13

2.4. The Effects of SOX on Earnings Management........19

2.5. Contributions....................................22

3 HYPOTHESES..............................................23

3.1. The Effect of Sarbanes-Oxley on Accrual

Management.......................................23

3.2. The Effect of Sarbanes-Oxley on Real

Management.......................................24

viii

Page

4 RESEARCH METHOD AND SAMPLE SELECTION....................25

4.1. Overview of Research Method......................25

4.2. Cross-sectional Probit Model.....................27

4.3. Estimation Models and Expectations...............29

4.4. Sample Selection.................................33

5 RESULTS.................................................36

5.1. Univariate Results...............................36

5.2. Multivariate Results.............................39

5.3. The Use of Accrual Management Following

Sarbanes-Oxley...................................43

5.4. The Use of Real Earnings Management Post-SOX.....45

6 CONCLUSIONS.............................................48

6.1. Summary..........................................48

6.2. Limitations......................................50

6.3. Future Research..................................50

REFERENCES.....................................................53

ix

LIST OF TABLES

Page

Table 1: Industry Distributions...............................57

Table 2: Univariate Analysis..................................60

Table 3: Comparison of Just Meet/Beat Firm-Years with

Just Miss Firm-Years.................................64

Table 4: Comparison of Real Earnings Management Levels

Between Suspect Firms and Non-Suspect Firms..........67

Table 5: Time Trends in Accrual Management....................69

1

CHAPTER 1

INTRODUCTION

1.1.Statement of Issues

1.1.1. Earnings Management Background

It is well documented that managers have strong capital

market incentives (Myers and Skinner 1999; Barth et al. 1999;

Skinner and Sloan 2000) to manage reported earnings. The vast

majority of prior earnings management literature has focused

managers’ use of accruals to manage earnings (see Healy and

Wahlen, 1999; Dechow and Skinner, 2000; Beneish, 2001; Fields et

al., 2001 for survey). However, managers also have the option

to manage earnings through real earnings management (i.e., sales

manipulation, reduction of discretionary expenditures, and

overproduction). Roychowdhury (2005) provides evidence that

suggests managers do in fact use real earnings management

(hereafter REM) to beat earnings benchmarks. Specifically,

Roychowdhury (2005) finds that firms who just meet/beat the

profit and earnings change benchmarks exhibit higher levels of

abnormal production costs and lower levels of discretionary

expenses when compared to other firms across the distribution of

earnings. Graham, Harvey, and Rajgopal (2005) survey 401

2

financial executives and find that executives prefer to use REM

to manage earnings rather than accruals management. This

evidence is puzzling because manipulating real activities to

meet short-term earnings benchmarks represents a sacrifice of

economic value to the extent that the manipulated activities

deviate from long-term optimal actions. Consistent with this

view, Gunny (2005) documents that engaging in REM negatively

impacts operating performance in subsequent years. Unlike REM,

accruals management has no implications for cash flows or long-

term performance, and it reverses in subsequent periods.

Therefore, accrual management appears to be a less costly form

of earnings management when compared to REM.

1.1.2. Sarbanes-Oxley and Earnings Management

Although REM may be more costly than accruals management,

several recent papers suggest that managers’ preference for

engaging in REM may be increasing in recent years. Ewert and

Wangenhofer (2005) analytically demonstrate that tightening

accounting standards increases the marginal benefit of REM.

Similarly, Graham et al. (2005) suggest that recent accounting

scandals and the passage of the Sarbanes-Oxley Act (SOX) may

have altered managers’ preference for using REM versus accruals

management to ease stakeholder concerns. In support of this

assertion, one interviewed executive reports a desire to “…go

out of their way to assure stakeholders that there is no

3

accounting based earnings management in their books.” In

addition to avoiding the perception of being an “accounting

manipulator,” managers may avoid using aggressive accounting

methods to limit their own legal liability since SOX imposes

significant criminal and civil penalties on executives who

knowingly file false financial reports. Furthermore, accruals

management, unlike REM, is subject to auditor scrutiny. While

auditors can disallow aggressive accounting methods, they do not

have the ability to alter managers’ operational choices. Thus,

given that SOX increases the risk of engaging in accruals

management, firms may choose to use other forms of earnings

management that do not bear increased risk under SOX.

Recent evidence in Cohel, Dey, and Lys (2005) suggests that

earnings management behavior changed following SOX passage. In

particular, they create a composite measure of earnings

management using abnormal accrual proxies utilized in prior

research and other accounting ratios. Post-SOX, they find a

sharp decline in their earnings management measure. Their

evidence suggests that, on average, firms decreased earnings

management post-SOX. Given firms’ ability to use both accruals

management and REM to manage earnings, a decline in earnings

management post-SOX does not necessarily imply that all types of

earnings management activity declined, nor does it imply a

decrease in earnings management for firms with the strongest

4

incentives to manage earnings – e.g., those firms that absent

earnings management would just-miss earnings benchmarks.1 This

study investigates whether managers’ use of accrual

manipulations and REM to beat earnings benchmarks changed in the

wake of recent accounting scandals and the passage of SOX.

1.2.Summary of Research Methods and Results

1.2.1. Summary of Research Methods

To identify firms with stronger incentives to manage

earnings, I focus on firms with earnings around the three common

earnings benchmarks – profit, earnings change, and analysts’

forecasted earnings. My sample includes firm-year observations

for these just-miss and just-beat firms from 1987 to 2004. I

divide the sample into pre-SOX (1987 – 2001) and post-SOX

periods (2003-2004) and eliminate observations from 2002 since

SOX was effective the third-quarter of 2002.2

To provide evidence on the change in earnings management

following SOX, I estimate a probit regression that relates a

firm’s probability of beating an earnings benchmark with the

firm’s abnormal accruals, abnormal production costs, and

abnormal discretionary expenses. I use the Jones (1991) model

to estimate discretionary accruals and linear models presented

1 Cohen et al. (2005) do not examine the time-series properties of their earnings management measure for just miss or just beat firms. 2 The actual year deleted varies depending on each firm’s month of fiscal year-end. For firms with fiscal years ending from January to June and October to December, I delete fiscal year 2002. For firms with fiscal years ending in July or August, I delete fiscal year 2003.

5

by Roychowdhury (2005) to estimate REM measures for production

costs and discretionary expenses. I interact measures of

abnormal accruals, abnormal production costs, and abnormal

discretionary expenses with an indicator variable for post-SOX

firm-years to measure the incremental effect of SOX on the use

of accrual management and REM to `eat earnings benchmarks. If

REM increases post-SOX, then I expect to find a positive and

significant coefficient on the interactions of REM measures with

the post-SOX indicator variable. Similarly, if accruals

management declines post-SOX, I expect to find a significant

negative coefficient on the interaction of abnormal accruals

with the post-SOX indicator variable.

1.2.2. Summary of Results

Results indicate that the associations between abnormal

accruals and beating the profit, earnings change, and analysts’

forecast benchmarks do not change post-SOX. In contrast to

Cohen et al. (2005), who conclude that earnings management, on

average, declined post-SOX, this evidence suggests that SOX had

no significant effect on the use of accrual management to beat

earnings benchmarks. Regarding the use of REM to beat earnings

benchmarks, results indicate an increased association between

REM and the propensity to beat the profit and earnings change

benchmarks. I find no change in the association between REM and

the propensity to beat the analysts’ forecast benchmark. In

6

sum, results indicate a relative shift to REM for benchmark

beaters post-SOX, but that earnings management, on average, has

not declined for firms with strong incentives to manage

earnings.

1.3. Contributions

This paper contributes to the earnings management

literature in two ways. First, prior research suggests that

managers use several earnings management methods to beat

earnings benchmarks. This study demonstrates how regulatory

intervention influences how firms’ beat earnings benchmarks. In

particular, results suggest that post-SOX the associations

between abnormal production costs and beating earnings

benchmarks increase relative to the association between abnormal

accruals and beating earnings benchmarks. Second, SOX was

designed to limit opportunistic behavior by managers. Since REM

represents a sacrifice of future economic benefit to improve

short-term financial reporting, investors have incentives to

identify and limit managers’ use of REM. To date, the effects

of SOX on financial reporting decisions and managers’ actions

are still largely unknown. This paper suggests that SOX

resulted in an increase of REM, arguably a more costly and less

attractive method to beat benchmarks.

7

1.4. Organization of the Dissertation

The remainder of the paper is organized as follows: Section

2 reviews prior literature and Section 3 develops testable

hypotheses. In Section 4, I describe the sample selection and

research method. I present results in Section 5 and discuss

conclusions and implications for future research in Section 6.

8

CHAPTER 2

LITERATURE REVIEW

In this chapter I review the extant accounting literature

related to this study’s predictions and empirical tests. I also

discuss this study’s contributions to the accounting literature.

I begin this literature review in section 2.1 by reviewing

studies that empirically define earnings management. In section

2.2 I discuss the various incentives that managers have to beat

earnings benchmarks. In section 2.3 I review the streams of

literature that provide evidence regarding earnings management

to beat earnings benchmarks, both through the use of accrual

management and real earnings management. I discuss related

studies that investigate the effects of SOX on financial

reporting in section 2.4. Finally, I discuss this study’s

contributions to the accounting literature in section 2.5.

2.1. Empirical Definitions of Earnings Management

The vast majority of prior earnings management research

investigates earnings management from the perspective of accrual

management. Likewise, several widely accepted definitions of

earnings management tend to define earnings management in terms

of accruals management. For instance, Schipper (1989) defines

earnings management as:

9

… [A] purposeful intervention in the external financial reporting process, with the intent of obtaining some private gain (as opposed to, say, merely facilitating the neutral operation of the process.

Similarly, Healy and Whalen (1999) define earnings management as

follows:

Earnings management occurs when managers use judgment in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the underlying economic performance of the company, or to influence contractual outcomes that depend on reported accounting numbers.

Both of these definitions refer to the financial reporting

process as a context for earnings management. Accruals

management is the type of earnings management that takes place

inside the financial reporting process since Generally Accepted

Accounting Principles (GAAP) are based on the accrual method of

accounting.

REM does not fit precisely inside these widely cited

definitions of earnings management since it is an intervention

into the internal operational processes of the firm rather than

the external financial reporting processes. However, REM does

share an important characteristic used in each of the widely

used definitions of earnings management. Namely, managers

engage in REM with the intent to “obtain some private gain” and

to “mislead some stakeholders about the underlying economic

performance of the company”. In summary, accruals management is

10

the use mf managerial discretion over accounting choices with

the intent to influence reported accountine numbers; REM is the

use of managerial discretion over operational choices with the

intent to influence reported accounting numbers.

2.2. Incentives to Beat Earnings Benchmarks

2.2.1. Capital Market Incentives

A long line of literature exists that documents incentives

for managers to meet or beat earnings benchmarks (i.e. the zero

earnings, earnings change, and analysts’ forecast benchmarks).

One of the most documented incentives to meet or beat earnings

benchmapks comes from the capital markets.

Several papers have examined capital market reactions

around the earnings change benchmark. DeAngelo, DeAngelo, and

Skinner (1996) investigate the capital market reaction to

breaking a string of annual earnings increases. They find that

firms who miss the earnings change benchmark after beating the

benchmark in at least the nine previous years had on average a

–14% return in the benchmark miss year. Barth, Elliott, and

Finn (1999) find that firms with consecutive strings of annual

earnings increases have higher price- earnings multiples than

other firms. Similar to DeAngelo, DeAngelo, and Skinner (1996),

they also find that there is a significant decline in the price-

earnings multiple when the string of earnings increases is

broken. Myers and Skinner (1999) use quarterly earnings data

11

and find similar results. These papers suggest that the capital

markets reward firms who consistently beat the earnings change

benchmark and punish firms when they break and string of

consecutive earnings increases; thus giving managers an

incentive to manage earnings to beat the earnings change

benchmark.

Several papers have also investigated the capital market

incentives for managers to beat the analysts’ forecast

benchmark. Skinner and Sloan (2001) examine the difference in

the market responses to earnings surprises3 between growth

stocks4 and value stocks. They find that the market response to

positive earnings surprises is similar for both growth stocks

and value stocks. However, they also find that the market

reaction to negative earnings surprises is disproportionately

larger for growth stocks. This evidence suggests that managers

of growth stocks have strong incentives to meet/beat analysts’

forecasts. Kasznik and McNichols (2002) find the capital

markets reward firms that consistently beat analysts’ forecasts.

They show that firms who beat analysts’ forecast benchmark in a

given year have higher abnormal returns than those who miss the

benchmark. Additionally, firms who have beat the analysts’

forecast benchmark in the prior two years have higher abnormal

3 Skinner and Sloan define an earnings surprise as the difference between actual earnings and the consensus analysts� forecast. 4 Growth stocks are defined in terms of market-to-book ratio.

12

returns than those who have only beat the benchmark in the

current year. Bartov, Givoly, and Hayn (2002) find similar

results showing that firms who meet/beat the quarterly analysts’

earnings forecast have higher price premiums than firms that

miss this benchmark. Additionally, Mikhail, Walther, and Willis

(2004) find that firms who miss the analysts’ forecast benchmark

have higher cost of equity capital than firms that meet/beat

analysts’ expectations. These studies suggest that managers

have a strong incentive to beat the analysts’ forecast benchmark

and to continue to do so year after year.

Graham, Harvey, and Rajgopal (2005) survey 401 financial

executives and ask about their perceptions on the importance of

beating earnings benchmarks. They find that 65.2% of responding

executives say that beating the profit benchmark is important;

73.5% say that beating the analysts’ forecast benchmark is

important, and 85.1% say that beating the earnings change

benchmark is important. When asked why they believe beating

earnings benchmarks is important, 86.3% responded that beating

the earnings benchmarks builds credibility with the capital

markets, and 82.2% responded that beating the earnings

benchmarks helps maintain or increase stock prices. These

survey results strongly reflect a belief by executives that

beating earnings benchmarks is important to the capital markets.

13

2.2.2. CEO Compensation Incentives

Matsunaga and Park (2001) directly examine the effect of

missing or meeting earnings benchmarks on CEO cash compensation.

They find that meeting/beating the analysts’ forecast benchmark

and meeting/beating the earnings change benchmark has an effect

on CEO compensation. Their results suggest that CEOs have an

incentive to beat at least some earnings benchmarks in order to

increase their own personal wealth.

2.3. Earnings Management to Meet/Beat Earnings Benchmarks

2.3.1 Distributional Studies

Hayn (1995) is the first paper to document discontinuities

in the distributions around earnings benchmarks. She examines

the annual earnings distributions around the profit benchmark

and finds fewer than expected firm-years in the small loss

category and greater than expected firm-years in the small

profit category. Although her study does not specifically test

for earnings management, she notes that this result is

consistent with firms managing earnings to beat the profit

benchmark.

Burgstahler and Dichev (1997) also use the distributional

method to investigate discontinuities around the profit and

earnings change benchmarks, but unlike Hayn (1995) they develop

two theories to explain managers incentives to manage earning to

meet/beat these two earnings benchmarks – transactional cost

14

theory and prospect theory. Burgstahler and Dichev, similar to

Hayn (1995), find discontinuities around the profit and earnings

change benchmarks. They conclude that 8%-12% of firms with

small pre-managed earnings decreases manage earnings to beat the

earnings change benchmark. They also conclude that 30%-44% of

firms with small pre-managed losses manage earnings to beat the

profit benchmark. Burgstahler and Eames (1999) extend

Burgstahler and Dichev (1997) by documenting similar

discontinuities around the analysts’ forecast benchmark,

consistent with firms managing earnings upwards to meet/beat

this benchmark.

Degeorge, Patel, and Zeckhauser (1999) also examine

earnings management to exceed earnings benchmarks. Similar to

Burgstahler and Dichev (1997) and Burgstahler and Eames (1999),

they find evidence that managers act opportunistically to exceed

the profit, earnings change, and analysts’ forecast benchmarks.

They also evaluate the relative importance of each benchmark.

Their results suggest that managers view the profit benchmark as

the most important benchmark, followed by the earnings change

benchmark and then the analysts’ forecast benchmark. Brown and

Caylor (2005) reevaluate the relative importance of these three

earnings benchmarks and find that the importance of the

benchmarks appears to have shifted in recent years with the

15

analysts’ forecast benchmark becoming the most important

benchmark for managers to beat.

2.3.2. Earnings Management in Specific Contexts

Although studies such as Burgstahler and Dichev (1997) and

Degeorge et al. (1999) provide strong evidence for the existence

and pervasiveness of earnings management, neither study examines

how or why managers meet/beat the earnings benchmarks. To

answer these questions, many studies examine earnings management

to beat earnings benchmarks in specific contexts. Beaver,

McNichols, and Nelson (2003) examine earnings management to

meet/beat the profit benchmark in the property-casualty

insurance industry. Specifically, they examine managers’ use of

claim loss reserves to manage earnings. The insurance industry

provides a unique context to examine earnings management because

managers must establish a claim loss reserve based on estimates

of future claims. By underestimating (overestimating) the claim

loss reserve, managers can increase (decrease) current net

income. However, the accuracy of the claim loss reserve

estimates can eventually be determined as actual claims occur.

This allows researchers to compute the overestimation or

underestimation of the initial claim loss reserve. Beaver et

al. (2003) conclude that managers use the claim loss reserve in

an opportunistic manner to meet/beat the profit benchmark.

16

Beatty, Ke, and Petroni (2002) examine earnings management

in the banking industry. They compare the earnings change

distributions of publicly owned banks to privately owned banks.

They argue that publicly owned banks have greater incentives to

manage earnings due to capital market pressures. Their results

indicate that publicly held banks are more likely to just

meet/beat the earnings change benchmark through the use of loan

loss reserves and realized security gains and losses.

Phillips, Pincus, and Rego (2003) use deferred tax expense

(DTE) to detect earnings management around the three earnings

benchmarks. They argue that the tax code does not allow as much

managerial discretion as GAAP; thus, managing earnings upwards

creates a temporary book-tax difference. They find that DTE is

incrementally useful in detecting earnings management around all

three earnings benchmarks. Dhaliwal, Gleason, Mills (2004) also

use a tax methodology to examine earnings management to

meet/beat the analysts’ forecast benchmark. Specifically, they

investigate whether managers opportunistically use income tax

expense to boost earnings in order to meet analysts’

expectations. Their results indicate that managers reduce the

estimates of their effective tax rates in the fourth quarter to

avoid missing the analysts’ forecast benchmark.

Several other studies also suggest that firms manage

accruals upwards to meet/beat earnings benchmarks. Das and

17

Zhang (2003) investigate whether managers use their discretion

over reported accounting numbers to round up earnings to

meet/beat earnings benchmarks. They find that managers are more

likely to manipulate earnings to round up earnings if managers

expect that rounding up will meet/beat the profit, earnings

change, or analysts’ forecast benchmark. They present evidence

that managers achieve this rounding up by manipulating accruals.

Moehrle (2002) also finds evidence that firms manage accruals to

meet/beat earnings benchmarks. He documents that managers

reverse prior period restructuring charge accruals to meet/beat

the profit or analysts’ forecast benchmarks. He finds weaker

evidence for the earnings change benchmark.

2.3.3. Real Earnings Management

The vast majority of prior studies on earnings management

focus on the opportunistic use of accruals. Of the relatively

few studies investigating REM, most focus on managers’

opportunistic use of R&D to meet certain reporting goals. For

example, Baber et al. (1991) find that managers decrease R&D

spending when they face the prospect of reporting a small loss

or decreased earnings. Similarly, Bushee (1998) provides

evidence that managers reduce R&D expenses to avoid an earnings

decline. Dechow & Sloan (1991) investigate the link between CEO

horizon and R&D spending. They find that CEOs spend less on R&D

during their final years with the firm to improve short-term

18

performance. This evidence suggests that CEOs myopically

managing earnings to maximize personal wealth. Bens et al.

(2002) find that managers are willing to repurchase stock to

avoid EPS dilution from stock option exercises, and that

managers use reductions in R&D expenses, in part, to finance

these repurchases.

While most of the prior literature on REM focuses on R&D

expenses, a few papers provide evidence on other REM methods.

Thomas and Zhang (2002) investigate the relation between

inventory changes and the market inefficiency documented by

Sloan (1996). Their results suggest that managers overproduce

with the intention of lowering COGS and thus increasing

earnings. Gunny (2005) investigates the subsequent performance

of firms that engage in REM and finds these firms have lower

return on assets and lower cash flows in future years. This

evidence suggests that managers trade long-term performance for

short-term gains.

Roychowhury (2005) documents that managers engage in REM to

avoid reporting annual losses and annual earnings decreases.

Specifically, he finds that firms suspected of engaging in REM

to cross the profit and earnings change benchmarks exhibit

abnormally high production costs, abnormally low discretionary

expenses, and abnormally low cash flows from operations compared

to other firms in the earnings distribution. This evidence is

19

consistent with managers overproducing, offering aggressive

price discounts, and cutting discretionary expenses to beat the

profit and earnings increase benchmarks.

2.4. The Effects of SOX on Earnings Management

Congress passed SOX in July 2002 in response to a litany of

accounting scandals that had occurred over the previous year.

While SOX specifically targets fraudulent financial reporting,

it also likely impacts other aggressive accounting choices. SOX

increases the cost of engaging in accruals management, and thus

lowers the cost of REM relative to accruals management in three

specific ways. First, SOX requires CEOs and CFOs to personally

certify the correctness of their public financial statements,

and SOX significantly increases the criminal and civil penalties

for executives who knowingly file false statements. This

increased legal risk may discourage managers from engaging in

aggressive accruals management. Unlike accruals management, REM

is unlikely to result in criminal or civil penalties because REM

is an intervention into a firm’s internal operational process

rather than an intervention into a firm’s external financial

reporting process. Second, SOX seeks to increase monitoring by

severely restricting the types of non-audit work that a firm’s

audit company may perform and requiring audit committees to

approve other non-audit work. Additionally, financial

20

statements filed with the SEC must include a report of

independent accountants verifying that there has been no

impairment of auditor independence. This heightened focus on

auditor independence is likely to lead to more auditor scrutiny

of questionable accounting choices. Since auditors have the

ability to limit managers’ use of accruals management, increased

auditor independence increases the risk that auditors will

disallow accounting choices aimed at increasing earnings (i.e.,

accruals management). However, auditors have little or no

authority to challenge managers’ operational choices. Thus, the

increased risk of auditors disallowing aggressive accounting

treatments (i.e., accrual management techniques) may lead

managers to increase REM.

Third, Graham et al. (2005) provide anecdotal evidence that

managers engage in REM to avoid being viewed by shareholders as

an accounting manipulator. The flurry of accounting scandals

from late 2001 through 2002 along with the passage of SOX has

lead to an increase in public awareness of aggressive accounting

methods. This heightened shareholder scrutiny of accounting

choices also increases the advantages of REM since operational

choices are largely seen as separate from accounting choices.

Two recent studies provide evidence that suggests that

earnings management may have decreased post-SOX. Cohen et al.

(2005) use factor analysis to create an earnings management

21

measure based on three variations of the modified Jones model,

the ratio of the absolute value of accruals to the absolute

value of cash flows from operations, the ratio of the change in

accounts receivables to change in sales, the ratio of change in

inventory to the change in sales, and the frequency of special

items reported for the period. They report an upward trend in

their earnings management proxy in the pre-SOX period, followed

by a significant decline post-SOX. They conclude that, on

average, earnings management declined after SOX.

Lobo and Zhou (2005) investigate whether SOX affects

conservatism in financial reporting. Specifically, they focus

on whether firms exhibit more reporting conservatism in the

initial year of required CEO/CFO certification of financial

reports. Using the modified Jones model to estimate

discretionary accruals for a broad cross-section of firms, they

find that firms report lower discretionary accruals post-SOX.

They also find that negative security returns are more quickly

incorporated into financial statement net income than positive

security returns in the post-SOX period. They interpret their

results as providing preliminary evidence that managers are more

conservative post-SOX.

22

2.5. Contributions

In general, the extant literature indicates that managers

engage in REM to beat earnings targets even though future

performance may suffer. In addition, Ewert and Wagenhofer

(2005) show analytically that tighter accounting standards lead

to an increase in REM due to an increase in the marginal

benefits of engaging in earnings management. Schipper (2003)

also suggests that tightening accounting standards will lead to

a substitution effect between accrual manipulation and REM.

This study investigates whether tightening accounting standards

via SOX leads to an increase in REM and a decrease in accruals

management. Relative to prior research investigating the use of

REM to manage earnings and the change in earnings management

post-SOX, this study makes two important innovations. First,

prior studies suggest that, on average, earnings management

declined post-SOX. I investigate how SOX affects the earnings

management behavior of firms with strong incentives to manage

earnings (i.e., firms with earnings located around the earnings

benchmarks). Second, I test whether the preferences for

accruals management and REM change post-SOX. This is

particularly important since REM is likely a more costly form of

earnings management in terms of future firm performance.

23

CHAPTER 3

HYPOTHESES

In this chapter I state my two testable hypotheses.

Section 3.1 discusses the use of accrual management to beat

earnings benchmarks following Sarbanes-Oxley. Section 3.2

discusses the use of REM to beat earnings benchmarks following

Sarbanes-Oxley.

3.1. The Effect of Sarbanes-Oxley on Accrual Management

Prior literature finds that, on average, accruals

management declines and accounting conservatism increases post-

SOX (Cohen et al. 2005, Lobo and Zhou 2005). Given these

findings and the increased cost of engaging in accrual

management post-SOX (e.g., increased executive liability,

increased monitoring, and increased investor awareness), I

expect that accrual management for benchmark firms will decrease

post-SOX. This leads to my first testable hypothesis:

Hypothesis 1: The use of accrual management to beat

earnings benchmarks declines following SOX.

24

3.2. The Effect of Sarbanes-Oxley on Real Management

A decline in the use of accrual management does not

necessarily imply that all types of earnings management will

decline post-SOX. Roychowdhury (2005) and Gunny (2005) document

that managers are willing to engage in REM prior to SOX. Given

firms’ willingness to engage in REM to beat earnings benchmarks,

an increase in the cost of engaging in accrual management may

simply result in a shift to REM. This forms my second

hypothesis.

Hypothesis 2: The use of real earnings management to beat

earnings benchmarks increases following SOX.

25

CHAPTER 4

RESEARCH METHOD AND SAMPLE SELECTION

In this chapter I describe the research method I employ to

test my hypotheses and explain my sample selection process. In

section 4.1, I provide a general overview of my research method

and sample selection method. I explain the cross-sectional

probit model that I use to test my hypotheses in section 4.2.

In section 4.3, I review the various estimation models I use to

estimate abnormal accruals, abnormal production costs, and

abnormal discretionary expenses. I also develop expectations

for results. Finally, I describe my sample selection method in

section 4.4.

4.1. Overview of Research Method

To investigate the effect of SOX on earnings management

behavior, I estimate abnormal accruals and REM proxies for a

sample of firms from 1987 – 2004. I examine three types of REM

– overproduction, sales manipulation, and discretionary expense

manipulation investigated in prior literature (e.g.,

Roychowdhury 2005, Gunny 2005). Managers have the option of

cutting discretionary expenses such as sales, general, and

administrative expense (SG&A), research and development expenses

(R&D), and advertising expense to manage earnings. Although

26

SG&A is not entirely discretionary, many discretionary items

such as employee training expense, travel expenses, and certain

types of maintenance are commonly included in SG&A. Cutting

these discretionary expenses increases cash flows from

operations (CFO) and operating income in the current period. In

addition to reducing discretionary expenses, managers of

manufacturing firms may choose to overproduce to manage earnings

upward. Increased production levels spread fixed costs across

more units, thus lowering cost of goods sold and increasing

gross margin and net income. While reported net income

increases in the current period because of overproduction, cash

flows from operations decrease since the firm incurs increased

production and holding costs for the additional units produced.

This results in lower than normal cash flows from operations at

a given level of sales and higher production costs relative to

sales.

Managers may also seek to manage earnings by artificially

boosting sales through aggressive price discounts. Aggressive

price discounts (i.e., discounts more extensive than those

offered in the normal course of business) accelerate sales into

the current period and thus increase sales revenue and net

income. Using this strategy, sales revenue per unit would be

lower than normal, whereas production costs relative to sales

would be higher than normal.

27

4.2. Cross-sectional Probit Model

I focus on firms with relatively stronger incentives to

manage earnings by restricting my sample to firms with earnings

around three common earnings benchmarks – profit, earnings

change, and analysts’ forecasted earnings. Specifically, to

determine whether SOX has an effect on earning management

choices, I use the following probit regression that relates a

firm’s probability of meeting/beating a given earnings benchmark

with the firm’s abnormal accruals, abnormal production costs,

and abnormal discretionary expenses in the pre-SOX and post-SOX

periods.

BM = a + 1b AbAccr + 2b Abprod + 3b AbDisc + 4b SOX + 5b AbAccr * SOX + 6b Abprod * SOX + 7b AbDisc * SOX +

8b CFO + 9b NOA + E (1) where:

Profit Benchmark: BM equals one for firm-years with

scaled earnings (NI t / TA 1−t ) greater than or equal to 0

but less than 0.01, and BM equals zero for firm-years with scaled earnings greater than or equal to –0.01 but less than 0 (Burgstahler and Dichev 1997; Phillips et al. 2003; Ayers et al. 2005). Earnings Change Benchmark: BM equals one for firm-

years with scaled earnings changes (NI t - NI 1−t / TA 1−t )

greater than or equal to 0 but less than 0.005, and BM equals zero for firm-years with scaled earnings changes greater than or equal to –0.005 but less than 0 (Burgstahler and Dichev 1997; Roychowdhury 2005). Analysts’ Forecast Benchmark: BM equals one for firm-years with (EPS – forecasted EPS) greater than or equal to 0 but less that 0.01, and BM equals zero for

28

firm-years with (EPS – forecasted EPS) greater than or equal to –0.01 but less than 0. Forecasted EPS is defined as the most recent analyst forecast prior to the announcement of annual earnings (Ayers et al. 2005). AbAccr (abnormal accruals) is the difference between total accruals and estimated expected accruals using the Jones (1991) model (discussed below). AbProd (abnormal production costs) is the difference between a firm’s actual production costs (Costs of goods sold + Change in inventory) and estimated expected production costs (discussed below). AbDisc (abnormal discretionary costs) is the difference between a firm’s actual discretionary costs (SG&A + R&D + Advertising expenses) and estimated expected discretionary costs (discussed below). SOX equals one for Post-Sox years (i.e., 2003-2004), and zero otherwise. CFO is cash flow from operations (Compustat Data #308). ∆CFO is the change is cash flow from operations from year t-1 to year t. ∆CFO replaces CFO for analyses using the earnings change benchmark. NOA is net operating assets defined as total shareholder’s equity – cash and short-term investments + total debt.

29

4.3. Estimation Models and Expectations

I estimate a firm’s expected level of accruals using the

Jones (1991) model. 5

Accruals t /A 1−t = α 0 + α 1 *(1/ A 1−t ) + β1 *(∆S t / A 1−t ) + β 2 *(PPE t /A 1−t ) + ε t (2)

where:

Accruals t is total accruals for year t, and

A 1−t is total assets at the end of period t-1, and

∆S t is the change in sales from period t-1 to period t, and

PPE t is property, plant, and equipment at the end of period t.

I estimate equation (2) by industry and year and include an

unscaled intercept, α 0 , to force the mean abnormal accruals for

each industry-year to be zero. I use the parameter estimates

from equation (2) to estimate the firm’s expected accruals. I

then estimate abnormal accruals as the difference between the

firm’s actual accruals and expected accruals as follows:

AbAccr t = Accruals t /A 1−t - [α 0 + α 1 *(1/ A 1−t ) + β1 *(∆S t / A 1−t ) + β 2 *(PPE t /A 1−t )] (3)

To the extent that firms use discretionary accruals to beat

earnings benchmarks, I expect to find a positive coefficient on

AbAccr.

5 Results using the modified Jones model (Dechow et al., 1995) are nearly identical to results when using the Jones model.

30

Following Dechow et al. (1998) and Roychowdhury (2005), I

estimate the expected level of production costs using the

following model:

PROD t / A 1−t =α 0 +α 1 *(1/ A 1−t )+β1 *(S t / A 1−t )+β 2 *(∆S t / A 1−t )+β 3 *(∆S 1−t / A 1−t )+ε t (4)

where:

PROD t is total production costs for period t, and

S t is sales revenue for time period t (Compustat Data #12), and

∆S 1−t is the change is sales revenue from period t-2 to period t-1.

I define all other terms the same as defined in equation 2. I estimate equation (4) by industry and year and use the

parameter estimates from equation (4) to determine the firm’s

expected production costs. I then calculate AbProd as the

difference between the firm’s actual production costs (i.e., the

sum of Cost of goods sold and Change in inventory) and its

expected production costs. AbProd represents a firm’s abnormal

production costs relative to other firms in the same industry.

Concurrent literature (Roychowdhury 2005, Gunny 2005) suggests

that managers engage in REM to beat earnings benchmarks. To the

extent that managers are willing to engage in overproduction and

sales manipulation to beat earnings benchmarks, I expect the

coefficient on AbProd to be positive.

31

I estimate discretionary expenses using the following model

by industry and year (Roychowdhury, 2005):

DISEXP t /A 1−t = α 0 + α 1 *(1/ A 1−t ) + β1 *(S 1−t / A 1−t ) + ε t (5)

where:

DISEXP t is discretionary expenses for period t, and

S 1−t is sales revenue for time period t-1.

All other terms are as defined in equation 2.

Using lagged sales rather than current sales to estimate

discretionary expenses mitigates one potentially complicating

issue. If firms opt to manage earnings by increasing sales in a

given year, then discretionary expenses would appear abnormally

low even if they have not been managed. Using lagged sales

alleviates this problem to the extent that firms are not located

around an earnings benchmark in successive years. I expect to

find a negative coefficient on AbDisc, since lowering expenses

in the current period results in higher current period income.

SOX denotes whether a firm-year occurs before or after the

passage of the Sarbanes-Oxley Act, and thus, represents the

incremental propensity for a firm to beat a benchmark post-SOX.

Cohen et al. (2005) document a sharp decline in earnings

management, on average, post-SOX. Additionally, Lobo and Zhou

(2005) find an increase in accounting conservatism post-SOX. To

the extent that (1) Sox inhibited firms’ abilities to beat

32

benchmarks using accrual management and (2) REM was not a viable

method for a subset of firms to beat benchmarks, I anticipate a

negative coefficient for SOX.

Hypothesis 1 predicts that post-SOX firms decreased their

use of accruals management to meet/beat earnings benchmarks. If

firms decreased their use of accrual management to meet/beat

earnings benchmarks post-SOX, the coefficient on AbAccr * SOX

should be negative. Hypothesis 2 predicts that SOX caused firms

to increase their use of REM to meet or beat earnings

benchmarks. If firms engaged in more REM through increased use

of overproduction and/or sales manipulations, then the

coefficient on Abprod * SOX , should be positive. Likewise, if

firms engage in more discretionary expenses manipulation

following SOX, then the coefficient on AbDisc* SOX should be

negative. I include either cash flows or change in cash flows

in my model to control for the effect of a firm’s cash flow on

the firm’s need to use accrual management or REM to meet or beat

a benchmark (Phillips et al. 2003). I expect that the

coefficient on CFO (∆CFO) will be positive, since firms with

higher cash flows should be more likely to beat benchmarks.

Finally I include net operating assets (NOA) to control for a

firm’s level of accrual flexibility. The higher a firm’s net

operating assets, the lower their ability to manage accruals to

33

beat earnings benchmarks (Barton and Simko 2004). However,

given a firm’s ability to use REM to beat benchmarks and to

walk-down analysts’ forecasts, I make no prediction about the

sign of NOA.

4.4. Sample Selection

I collect financial data from Compustat and analyst

forecast data from I/B/E/S. I require that cash flows from

operations are available from the Statement of Cash Flows, which

restricts the sample to post-1986 firm-years. I also require

sample firm-years to have sufficient data available to compute

the necessary variables used for estimations of expected

accruals, production costs, and discretionary expenses. Since

SOX applies to domestically traded firms, I exclude foreign

firms from the sample. I also exclude regulated industries (SIC

codes 4400 through 4999) and banks and financial institutions

(SIC codes 6000 through 6999). These firms operate in a

different regulatory environment than other firms and likely

have different earnings management incentives. Thus, I would

expect SOX to affect regulated firms differently than other

firms. Since I estimate expected accruals, production costs and

discretionary expenses for each industry-year, I require at

least 10 observations for each industry-year. I use two-digit

SIC codes to assign each firm’s industry. Panels A, B, and C of

Table 1 list the number of firms in each two-digit SIC code for

34

my three samples. Panel A lists the SIC codes for firms in the

profit benchmarks sample. Panel B lists the SIC codes for firms

in the earnings change benchmark sample. Finally, Panel C lists

the SIC codes for the firms in the analysts’ forecast benchmark

sample.

The modal industry represented in the profit benchmark

sample is Electrical and Other Electrical Equipment (SIC code

36) with 379 firm-year observations. Measuring Instruments,

Photo Goods, and Watches (SIC code 38) has the second highest

number of observations with 322 firm-years. The twenty

industries with the highest representation account for 83.3% of

all observations in the profit benchmark sample.

The earnings change benchmark sample has a similar

distribution to the profit benchmark sample. Like the profit

benchmark sample, Electrical and Other Electrical Equipment (SIC

code 36) is the modal industry with 473 firm-year observations.

Industrial and Commercial Machinery and Computer Equipment (SIC

code 35) is the second highest represented industry with 471

observations. The twenty industries with the highest

representation account for 82.1% of all earnings benchmark

sample observations.

Like the profit and earnings change samples, Electrical and

Other Electrical Equipment (SIC code 36) is the modal industry

for the analysts’ forecast benchmark sample with 940 firm-year

35

observations. Measuring Instruments, Photo Goods, and Watches

(SIC code 38) has the second highest number of observations with

794 firm-years. The twenty industries with the highest number

of observations account for 87.1% of the entire analysts’

forecast benchmark sample.

Insert Table 1 here

36

CHAPTER 5

RESULTS

In this chapter I present and interpret results for my two

testable hypotheses. Each hypothesis is individually tested for

the three earnings benchmarks. I also test hypotheses 2 for two

types of REM (e.g., production costs management and

discretionary costs management). In section 5.1, I present

univariate results for the effects of abnormal accruals,

abnormal production costs, abnormal discretionary expenses on

beating the earnings benchmarks pre-SOX and post-SOX. In

section 5.2, I present multivariate results and I also reconcile

these results with prior literature. In section 5.3, I review

the results for my first hypothesis and I reconcile these

results with prior literature. In section 5.4, I present

results related to my second hypothesis.

5.1. Univariate Results

Table 2 presents descriptive statistics for each of the

three benchmark samples. Imposing all of the data requirements

results in a sample of 3,434 firm-years around the profit

benchmark. 2,235 firm-years just meet/beat (i.e., .00 < itE <

.01) the profit benchmark, and 1,199 firm-years just miss (i.e.,

-.01 < itE < .00) the benchmark. I further separate the sample

37

into Pre-SOX and Post-SOX periods to examine changes over time.

Panel A indicates that there is no statistical difference in the

means between the just miss abnormal production levels and the

just beat abnormal production levels pre-SOX . However, there

is a statistically significant difference in the mean abnormal

production levels post-SOX (p = 0.0446). This is consistent

with firms increasing their management of production costs to

beat the profit benchmark post-SOX. There is no statistical

difference in the pre-SOX or post-SOX means for abnormal

accruals or abnormal discretionary expenses. However,

univariate comparisons of just miss to just beat firms are a

weaker test than multi-variate probit analyses since the

univariate analyses do not control for cash flows or the effect

of the various earnings management techniques on one another.

Insert Table 2 here

Panel B presents descriptive statistics for the 5,397 firm-

years located around the earnings change benchmark. 3,208 firm-

years just meet/ beat (i.e., .00 < ∆ itE < .010) the earnings

change benchmark, and 2,189 firm-years just miss (i.e., -.010 <

∆ itE < .00) the benchmark. Again, I separate the sample in pre-

SOX and post-SOX periods. The earnings change benchmark

exhibits the same pattern for mean abnormal production levels as

the profit benchmark. There is no statistical difference in the

mean abnormal production levels pre-SOX (p = 0.3643), but there

38

is a statistically significant difference post-SOX (p = 0.0829).

This is consistent with firms increasing their management of

production costs to beat the earnings change benchmark post-SOX.

Similar to the profit benchmark, there is no statistical

difference in the pre-SOX or post-SOX means for abnormal

accruals or abnormal discretionary expenses.

Panel C presents the descriptive statistics for the

analysts’ forecasted earnings benchmark. 3,751 firms just meet

or beat (i.e., .00 < itEPS < .01) the benchmark, and 2,934 firms

just miss (i.e., -.01 < itEPS < .00) the benchmark. Unlike the

profit and earnings change benchmarks, mean abnormal production

levels pre-SOX and post-SOX show a significant decline across

the analysts’ forecast benchmark. Thus, univariate analyses

provide no evidence that firms engage in REM to beat the

analysts’ forecasted earnings benchmark. Again like the profit

and earnings change benchmarks, there is no statistical

difference in the pre-SOX or post-SOX means for abnormal

accruals or in the pre-SOX means for abnormal discretionary

expenses. However, there is a statistical difference (p =

0.0645) between the means in the post-SOX samples. This

indicates that post-SOX, firms that just beat the analysts’

forecast benchmark have higher discretionary expenses than firms

that just miss the analysts’ forecast benchmark. This result is

39

not consistent with firms opportunistically managing

discretionary expenses.

5.2. Multivariate Results

Table 3 presents the results for estimating equation (1)

for firms that just meet or beat versus firms that just miss the

three common earnings benchmarks. The coefficient on AbAccr,

the measure of abnormal accruals pre-SOX, is positive and

significant for all three earnings benchmarks. Phillips et al.

(2003) find a similar association between measures of

discretionary accruals (i.e., total accruals, modified Jones

accruals, and forward-looking accruals) and the propensity to

beat the three earnings benchmarks. The positive coefficient on

AbAccr is consistent with firms managing accruals to cross the

earnings benchmarks.

Insert Table 3 here

The coefficient on Abprod, the measure of abnormal

production costs pre-SOX, is not significantly different than

zero for any of the three benchmarks with (p = .7160) for the

zero benchmark, (p = .1724) for the earnings change benchmark,

and (p = .9076) for the analysts’ forecast benchmark.6 The

insignificant coefficients on Abprod suggest that abnormal

production costs had no significant effect on the likelihood of

beating the earnings benchmarks pre-SOX. These results are

6 All p-values are reported as one-tail values.

40

inconsistent with Roychowdhury (2005) who finds that firms who

just meet or beat the profit benchmark7 exhibit higher levels of

abnormal production costs compared to other firms across the

earnings distribution.8 He concludes that firms who just meet or

beat the profit benchmark manipulate their production operations

to cross the benchmark threshold. Prior literature (Burgstahler

and Dichev 1997, Burgstaher and Eames 1999, Phillips et al.

2003, Skinner and Sloan 2001, Kasznik and McNichols 2002)

documents that firms around the earnings benchmarks have strong

incentives to manage earnings to beat those benchmarks. It is

unclear what incentives firms located further away from the

benchmarks have to manage earnings (e.g., income smoothing;

taking a big bath). Thus, it is difficult to draw conclusions

regarding earnings management to beat earning benchmarks when

comparing the abnormal production costs of firms that just-beat

earnings benchmarks to all other firms. Comparing just-beat and

just-miss firms focuses the analysis on firms with similar

earning management incentives and earnings properties. Thus, my

tests are less susceptible to alternative interpretations

To reconcile my results with Roychowdhury (2005) I

partially replicate his analysis using my sample. I present my

7 Roychowdhury (2005) also finds weaker results suggesting firms use production cost manipulations to meet/beat the earnings change benchmark. However, his main results are for the profit benchmark. 8 Roychowdhury (2005) does not specifically compare just miss to just beat firms. Instead, he compared firms who just beat earnings benchmarks to firms in the 29 surrounding earnings bins.

41

results in Table 4. First in panel A, I replicate his results

for the use of production cost management to beat the profit

benchmark by using OLS regression on the following regression

equation:

Abprod = a + 1b Suspect + 2b AbMTB + 3b AbSize + 4b AbNI + E (6)

where:

AbProd (abnormal production costs) is defined as the difference between a firm’s actual production costs and expected production costs. Expected production costs are estimated using the industry-year regression: Prod t = a 0 + a1 *(1/A 1−t ) + b1 *Sales t + b 2 *∆Sales t + b 3 *∆Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1. Suspect is an indicator variable taking on the value of 1 if the firm-year observations has scaled earnings (EBEI t / TA 1−t ) greater than or equal to 0 but less than

0.01. AbMTB (abnormal market-to-book) is the firm MTB subtracted from the industry mean MTB. AbSize (abnormal size) is the logarithm of market value of equity subtracted from the industry mean logarithm of market value of equity. AbNI (abnormal net income) is the scaled income before extraordinary items subtracted from the industry mean scaled income before extraordinary items.

The sample includes 18,546 observations with earnings

before extraordinary items between –7.5% and 7.5% of beginning

of the year total assets. Like Roychowdhury, I find that firms

that just meet/beat the profit benchmark exhibit significantly

higher abnormal production costs (t-stat = 2.53, p-value =

42

0.0115) compared to the much larger distribution of firms (i.e.,

not only the just-miss firms). Accordingly, the contrary

conclusions in Roychowdhury (2005) appear to be attributable to

the comparison of just-beat firms to a larger comparison group

of firms rather than those firms that just-miss the earnings

benchmark. I conclude that the differences in results are not

due to unique characteristics of my sample.

Insert Table 4 here

I predict that the coefficient on AbDisc, the measure of

abnormal discretionary expenses, will be negative to the extent

that firms manage discretionary expenses opportunistically. The

coefficient on AbDisc is not statistically significant for the

profit (p= 0.7207), earnings change (p = .2027), or analysts’

forecast (p = 0.7631) benchmarks.

This evidence is also inconsistent with Roychowdhury (2005)

who concludes that firms manage discretionary expenses downward

to meet/beat the profit benchmark. I again partially replicate

Roychowdhury’s analysis. In panel B of Table 4, I present

results of an OLS regression on the following regression

equation:

AbDisc = a + 1b Suspect + 2b AbMTB + 3b AbSize + 4b AbNI + E (7)

where:

AbDisc (abnormal discretionary costs) is defined as the difference between a firm’s actual discretionary

43

costs and expected discretionary costs. Expected discretionary costs are estimated using the industry-year regression: Disc t = a 0 + a1 * (1/A 1−t ) + b1 * Sales 1−t + ε t . All other terms are defined as described for equation (6).

I use the same 18,546 firm-year observation sample

described above for equation (6). Similar to Roychowdhury, I

find that firms who just meet/beat the profit benchmark exhibit

lower levels of abnormal discretionary expenses. The

coefficient on Suspect is negative and significant (t-stat = -

3.69, p-value = 0.0002). I conclude that the differing results

are attributable to differences in method and not a result of

unique characteristics in my sample.

Returning to Table 3, the coefficient on SOX is negative

for all three benchmarks, and is significant for the profit (p =

.0118) and analysts’ forecast benchmark (p = .0006). The

coefficient for the earnings change benchmark (p = .1156)

benchmark only approaches conventional significance levels.

This evidence suggests that firms are less likely to beat the

earnings benchmarks post-SOX. This is consistent with Lobo and

Zhou (2005) who find that accounting conservatism has increased

following SOX.

5.3. The Use of Accrual Management Following Sarbanes-Oxley

Hypothesis 1 predicts that firms decreased their use of

accrual management to meet or beat earnings benchmarks following

44

SOX. Results do not support this hypothesis. The coefficient

on AbAccr * SOX is insignificant for the profit benchmark (p =

0.9064), the earnings change benchmark (p = 0.8813), and the

analysts’ forecast benchmark (p = 0.8519). These results

indicate that SOX had little effect on the use of accrual

manipulations for these firms to meet or beat earnings

benchmarks.

These results are inconsistent with evidence presented by

Cohen, Dey, and Lys (2005), who find that the level of earnings

management, including discretionary accruals, declines post-SOX.

However, they focus on a broad cross-section of firms across the

entire earnings distribution, while I focus on firms with strong

incentives to manage earnings (i.e., firms around the earnings

benchmarks). To reconcile my results with Cohen et al. (2005),

I examine whether the time-series properties of my abnormal

accrual measure is similar to the time-series properties of the

earnings management metric used by Cohen et al. (2005).9 I

tabulate my results in Table 5.

Despite the fact that I use annual data while they use

quarterly data, I find a significantly positive time trend (t-

stat = 6.75, p-value = <.0001) indicating a rise in the use of

accrual management from the beginning of my sample in 1987 until

9 Cohen et al. (2005) construct an earnings management measure based on several discretionary accruals models and financial ratios. Using quarterly data, they find a significant decline in earnings management post-SOX.

45

the passage of SOX. Post-SOX, I find, on average, a

statistically significant decline in abnormal accruals (t-stat =

-3.08, p-value = 0.0021). This evidence indicates that the use

of discretionary accruals has declined overall post-SOX, but my

other analysis indicates that accrual management has not declined

for firms with strong incentives to manage earnings (i.e., firms

close to the earnings benchmarks).

Insert Table 5 Here

5.4. The Use of Real Earnings Management Post-SOX

Hypothesis 2 predicts that firms increased their use of REM

to meet or beat earnings benchmarks. Consistent with this

hypothesis, the coefficient on Abprod * SOX is positive and

significant for the profit (p = .0321) and earnings change (p =

.0477) benchmarks, indicating an increase in the use of

overproduction and/or sales manipulations to beat these

benchmarks following SOX. For the analysts’ forecast benchmark,

Abprod * SOX is positive as predicted but not significant (p =

.3136). It is not surprising that results for the analysts’

forecast benchmark are weaker than the other benchmarks. Unlike

accrual manipulations, production levels and sales cannot be

easily or quickly adjusted at the end of the year to meet the

analysts’ earnings forecast. Instead, they must be adjusted

during the year. Thus, analysts have the opportunity to adjust

46

their forecasts to incorporate changes in production levels and

sales. The profit and earnings change benchmarks are static

targets that do not change during the year. Therefore, managers

should be better able to use REM to meet/beat these two

benchmarks.10

Hypothesis 2 also predicts that firms will increase their

use of discretionary expenses manipulation to meet or beat

earnings benchmarks following SOX. My results do not support

this hypothesis. For all three earnings benchmarks, the

coefficient for AbDisc* SOX is insignificant. The coefficients

on the profit, earnings change, and analysts’ forecast

benchmarks have p-values of 0.5544, 0.7928, and 0.9465

respectively. These results indicate that SOX has no

significant effect on the use of discretionary expenses

manipulation to meet/beat earnings benchmarks.

Consistent with prior literature, the coefficients on CFO,

the control variables for firm cash flows, are positive and

significant for the profit (p = <.0001) and analysts’ forecast

(p = <.0001) benchmarks. Likewise the coefficient on ∆CFO is

positive and significant (p = <.0001) for the earnings change

benchmark. Finally, the coefficients on NOA, the control

variable for accrual flexibility, are positive for all three

10 Firms also have the option to walk down analysts’ forecasts (Richardson et al. 2004), which is likely to be much less costly than engaging in REM.

47

benchmarks. However, they are only significant for the profit

(p = .0010) and earnings change (p = .0009) benchmarks. This

result seems to indicate that higher levels of net operating

assets increase the probability of beating the profit and

earnings change benchmarks. The NOA coefficient for the

analysts’ forecast benchmark is insignificant (p = .5963)

indicating that the level of net operating assets has no

significant effect on a firm’s probability of beating the

analysts’ forecast benchmark.

48

CHAPTER 6

CONCLUSIONS

In this final chapter, I summarize the issues,

contributions, methods, and results of this study. I also

acknowledge the limitations of this study and offer some areas

for future research. In section 6.1, I offer a summary of the

study. Limitations are discussed in section 6.2. I conclude

with ideas for future research in section 6.3.

6.1. Summary

This study investigates whether managers alter their

earnings management behavior following SOX. Specifically, I

test whether REM (overproduction, sales manipulation, and

discretionary expenses manipulation) increased and whether

accrual manipulation decreased post-SOX. I focus on firms with

high incentives to manage earnings by limiting my sample to

firms located around three common earnings benchmarks – profit,

earnings change, and analysts’ forecasted earnings. Focusing on

these firms allows for a powerful test of earnings management

behavior, since firms around the earnings benchmarks have clear

incentives to manage earnings to meet/beat those earnings

benchmarks.

49

I use a probit regression that relates a firm’s probability

of meeting/beating an earnings benchmark with the firm’s

abnormal accruals, abnormal production costs, and abnormal

discretionary expenses in the pre-SOX and post-SOX periods.

Results indicate that managers increase their use of production

cost manipulation to meet/beat the profit and earnings change

benchmarks post-SOX. However, results also indicate that SOX

has no significant effect on the use of accrual manipulations or

discretionary expense manipulations to meet/beat earnings

benchmarks.

This paper contributes to the earnings management

literature in two ways. First, this study documents how new

accounting regulation influences how firms’ beat earnings

benchmarks. In particular, results suggest that post-SOX the

associations between abnormal production costs and beating

earnings benchmarks increase while the association between

abnormal accruals and beating earnings benchmarks remain

unchanged. Second, SOX was intended to limit opportunistic

behavior by managers. Since REM represents a sacrifice of

future economic benefit to improve short-term financial

reporting, investors have incentive to identify and limit

managers’ use of REM. Additionally, these results should be of

interest of regulators who have an obligation to understand the

consequences, both intended and unintended, of new accounting

50

regulations. To date, the effects of SOX on financial reporting

decisions and managers’ actions are still largely unknown. This

paper suggests that SOX resulted in an increase of REM, arguably

a more costly/less attractive method to beat benchmarks.

6.2. Limitations

This study should be interpreted in light of the following

limitations. First, it is difficult to discern whether managers

have altered their use of REM due to SOX or due to increased

investor awareness of accounting choices resulting from the rash

of accounting scandals that preceded SOX. I acknowledge that it

is difficult to disentangle the effect of the accounting

scandals that preceded SOX from the effects of SOX itself.

Second, prior studies have documented that discretionary

accruals models, such as the Jones model and modified Jones

model, do a relatively poor job of detecting earnings management

(Dechow et al. 1995, Thomas and Zhang 1999, McNichols 2000). To

the extent that the inherent noise in abnormal accrual measures

does not change cross-temporally, this study’s analyses may be

less susceptible to the concerns associated with these measures.

6.3. Future Research

This study is part of an emerging stream of research

investigating the use of real earnings management. Since this

line of research is still largely in its infancy, there are many

51

fertile areas for continuing research. This paper tests only a

few of the numerous types of real earnings techniques available

to managers. Future research may extend the list of real

earnings techniques beyond those currently being reviewed in the

accounting literature.

Future researchers may also choose to investigate the

impact of real earnings management on a firm’s cost of capital.

Managers often engage in real earnings management for the short-

term benefits associated with beating benchmarks. However,

concurrent literature documents that there are long-term

performance penalties for engaging in real earnings management.

This dichotomy of short-term gains versus long-term penalties

creates a natural question in regard to how supplies of equity

capital and debt capital will react to real earnings management.

Lastly, I document an increase in certain types of real

earnings management following the implementation of Sarbanes-

Oxley. However, it is unclear whether this increase is

permanent or temporary in nature. If the increase in real

earnings management is in response to Sarbanes-Oxley, then I

would expect the increase to be permanent. On the other hand,

if the increase is in response to the rash of accounting

scandals that gave rise to Sarbanes-Oxley, then I would expect

the increase to last only as long as investors remain focused on

accountine-based manipulations. Given more time to accumulate

52

data, future research should be able to answer whether this is a

permanent consequence of Sarbanes-Oxley or just a temporary

reaction to the rash of accounting scandals prior to Sarbanes-

Oxley.

53

REFERENCES Ayers, B., J. Jiang, E. Yeung, 2005, Discretionary accruals and earnings management: An analysis of pseudo earnings targets. The Accounting Review forthcoming Baber, W., P. Fairfield, and J. Haggard, 1991, The effect of concern about reported income on discretionary spending decisions: The case of research and development. The Accounting Review 66, 818-829 Barth, M., J. Elliot, M. Finn, 1999, Market rewards associated with patterns of increasing earnings. Journal of Accounting Research 32, 387-314 Bartov, E., D. Givoly, and C. Hayn, 2002, The rewards to meeting-or-beating earnings expectations. Journal of Accounting and Economics 33 (2), 173-204 Beatty, A., B. Ke, and K. Petroni, 2002. Earnings management to avoid earnings declines and losses across publicly and privately held banks. The Accounting Review, 77, 547-570 Beaver, W., M. McNichols, and K. Nelson, 2003. Management of the loss reserve accrual and the distribution of earnings in the property-casualty insurance industry. Journal of Accounting and Economics, 35, 347-376 Beneish, M.D., 2001, Earnings management: A perspective. Managerial Finance 27, 3-17 Bens, D., V. Nagar, and M. Wong, 2002, Real investment implications of employee stock option exercises. Journal of Accounting Research 40, 359-393 Brown, L. and M. Caylor, 2005, A temporal analysis of quarterly earnings thresholds: Propensities and valuation consequences. The Accounting Review 80 (2), 423-440 Burgstahler, D. and I. Dichev, 1997, Earnings management to avoid earnings decreases and losses. Journal of Accounting and Economics 24, 99-126

54

Burgstahler, D., and M. Eames, 1999, Management of earnings and management forecasts. Working Paper, University of Washington Bushee, B., 1998, The influence of institutional investors on myopic R&D investment behavior. The Accounting Review 73, 305-333 Cohen, D., A. Dey, and T. Lys, 2005, Trends in earnings management and informativeness of earnings announcements in the pre- and post-Sarbanes-Oxley periods, Working Paper, University of Southern California Das, S., and H. Zhang, 2003, Rounding-up in reported EPS, behavioral thresholds, and earnings management. Journal of Accounting and Economics 35, 31-50 DeAngelo, H., L. DeAngelo, and D. Skinner, 1996, Reversal of fortune: Dividend policy and the disappearance of sustained earnings growth. Journal of Financial Economics, 40, 341-371 Dechow, P., and R. Sloan, 1991, Executive incentives and the horizon problem: An empirical investigation. Journal of Accounting and Economics 14, 51-89 Dechow, P., R. Sloan, and A. Sweeney, 1995, Detecting earnings management. The Accounting Review 70, 193-225 Dechow, P., S. Kothari, and R. Watts, 1998, The relation between earnings and cash flows. Journal of Accounting and Economics 25, 133-168 Dechow, P. and D. Skinner, 2000, Earnings management: Reconciling the views of accounting academics, practitioners, and regulators. Accounting Horizons 14 (June), 235-250 Dechow, P., S. Richardson, A. Tuna, 2003, Why are earnings kinky? An examination of the earnings management explanation. Review of Accounting Studies 8, 355-384 Degeorge, F., J. Patel, and R. Zeckhauser, 1991, Earnings management to exceed thresholds. Journal of Business 72, 1-33 Ewert, R. and A. Wangenhofer, 2005, Economic effects of tightening accounting standards to restrict earnings management. The Accounting Review 80, 1101-1124

55

Fields, T., T. Lys, and L. Vincent, 2001, Empirical research on accounting choice. Journal of Accounting and Economics 31, 255-307 Graham, J., C. Harvey, and S. Rajgopal, 2005, The economic implications of corporate financial reporting. Working Paper, Duke University Gunny, K., 2005, What are the consequences of real earnings management. Working Paper, University of California - Berkeley Hayn, C., 1995, The information content of losses. Journal of Accounting and Economics, 20, 125-153 Healy, P. and J. Wahlen, 1999, A review of the earnings management literature and its implications for standard setting. Accounting Horizons 43, 365-383 Jones, J., 1991, Earnings management during import relief investigations. Journal of Accounting Research 29, 193-228 Kasznik, R., 1999, On the association between voluntary disclosure and earnings management. Journal of Accounting Research 37, 57-81 Kasznik, R. and M. McNichols, 2002, Does meeting earnings expectations matter: Evidence from analyst forecast revisions and share prices. Journal of Accounting Research 40 (3), 727-759 Lobo, G. and J. Zhou, 2005, Did conservatism in financial reporting increase after the Sarbanes-Oxley Act? Early evidence. Working Paper, University of Houston Lopez, T., and L. Rees, 2002, The effect of beating and missing analysts’ forecasts on the information content of unexpected earnings. Journal of Accounting, Auditing, and Finance 17 (2), 155-184 Matsunaga, S., and C. Park, 2001, The effect of missing a quarterly earnings benchmark on the CEO’s annual bonus. The Accounting Review, 76, 313-332 McNichols, M., 2000, Research design issues in earnings management studies. Journal of Accounting and Public Policy 19, 313-345

56

Mikhail, M., B. Walther, and R. Willis, 2004, Earnings surprises and the cost of equity capital. Working Paper, Duke University Moehrle, S., 2002, Do firms use restructuring charge reversals to meet earnings targets?. The Accounting Review 77 (2), 397-413 Myers, L. and D. Skinner, 1999, Earnings momentum and earnings management. Working Paper, University of Michigan Phillips, J., M. Pincus, and S. Rego, 2003, Earnings management: New evidence based on deferred tax expense. The Accounting Review 78 (2), 491-521 Richardson, S., S. Teoh, and P. Wysocki, 2004, The walkdown to beatable analyst forecasts: The roles of equity issuance and insider trading incentives. Contemporary Accounting Research 21, 885-924 Roychowdhury, S., 2005, Earnings management through real activities manipulation. Working Paper, Massachusetts Institute of Technology Schipper, K., 2003, Principles-based accounting standards. Accounting Horizons (March), 61-72 Skinner, D. and R. Sloan, 2002, Earnings surprises, growth expectations, and stock returns or Don’t let and earnings torpedo sink your portfolio. Review of Accounting Studies 7, 289-312 Thomas, J. and H. Zhang, 2002, Inventory changes and future returns. Review of Accounting Studies 7, 163-187

57

Table 1: Industry Distributions Industry distributions based on 2-Digit SIC codes for all three samples (profit, earnings change, and analysts’ forecast) for years 1987 – 2004. Panel A Industry distribution of firms that just miss or just meet/beat the profit benchmark. The sample is limited to firms with reported earnings before extraordinary items between –1.0% and 1.0% of total assets. The full sample is 3,434 firms.

SIC Code

Industry Description Number of Firm-Years

36 Electrical and Other Electrical Equip 379 38 Measuring Instr., Photo Gds, Watches 322 35 Ind and Comm Machinery, Computer Equip 318 73 Business Services 223 28 Chemicals and Allied Products 203 50 Durable Goods – Wholesale 189 20 Food and Kindred Products 142 59 Miscellaneous Retail 119 58 Eating & Drinking Places 111 33 Primary Metal Industries 99 34 Fabr Metal, Ex Machinery, Trans Equip 94 37 Transportation Equipment 93 30 Rubber & Misc Plastic Products 86 39 Miscellaneous Manufacturing 85 51 Nondurable Goods – Wholesale 79 13 Oil and Gas Extraction 79 23 Apparel & Similar Products - Fabrics 68 26 Paper and Allied Products 66 27 Printing, Publishing, and Allied 56 87 Engineering, Acct, Research, Mgmt & Rel 51 54 Food Stores 50 22 Textile Mill Products 50 80 Health Services 45 56 Apparel and Accessory Stores 44 99 Nonclassifiable Establishments 43 53 General Merchandise Stores 35 32 Stone, Clay, Glass, and Concrete Products 32 79 Amusement & Recreation Services 30 25 Furniture and Fixtures 27 78 Motion Pictures 26 29 Petroleum Refining and Related 24 All

Others 166

TOTAL 3434

58

Panel B Industry distribution of firms that just miss or just meet/beat the earnings change benchmark. The sample is limited to firms with reported earnings before extraordinary items between –1.0% and 1.0% of total assets. The full sample is 5,397 firms.

SIC Code

Industry Description Number of Firm-Years

36 Electrical and Other Electrical Equip 473 35 Ind and Comm Machinery, Computer Equip 471 28 Chemicals and Allied Products 418 38 Measuring Instr, Photo Goods, Watches 376 50 Durable Goods – Wholesale 290 20 Food and Kindred Products 281 73 Business Services 213 59 Miscellaneous Retail 195 37 Transportation Equipment 189 34 Fabr Metal, Ex Machinery, Trans Equip 175 51 Nondurable Goods – Wholesale 161 54 Food Stores 159 30 Rubber and Misc Plastic Products 155 27 Printing, Publishing, and Allied 153 58 Eating and Drinking Places 150 33 Primary Metal Industries 143 26 Paper and Allied Products 117 56 Apparel and Accessory Stores 107 23 Apparel & Similar Products – Fabric 105 22 Textile Mill Products 98 32 Stone, Clay, Glass, and Concrete Products 95 39 Miscellaneous Manufacturing 92 53 General Merchandise Stores 91 13 Oil and Gas Extraction 91 25 Furniture and Fixtures 71 87 Engring, Acct, Rsrch, Mgmt & Related 58 57 Home Furniture, Furninshings, & Equip 58 24 Lumber and Wood Products 57 79 Amusement and Recreation Services 56 80 Health Services 54 55 Auto Dealers and Gas Service Stations 52 All

Others 193

TOTAL 5397

59

Panel C Industry distribution of firms that just miss or just meet/beat the analysts’ forecast benchmark. The sample is limited to firms with actual earnings between –0.01 and 0.01 of the most recent analyst forecast of annual EPS. The full sample is 6,685 firms.

SIC Code

Industry Description Number of Firm-Years

36 Electrical and Other Electrical Equip 940 38 Measuring Inst, Photo Gds, Watches 794 35 Ind and Comm Machinery, Computer Equip 722 28 Chemicals and Allied Products 600 73 Business Services 416 20 Food and Kindred Products 282 59 Miscellaneous Retail 246 50 Durable Goods – Wholesale 224 56 Apparel and Accessory Stores 216 58 Eating and Drinking Places 174 37 Transportation Equipment 172 23 Apparel and Similar Products – Fabric 130 27 Printing, Publishing, and Allied 125 34 Fabr Metal, Ex Machinery, Trans Equip 122 51 Nondurable Goods – Wholesale 118 33 Primary Metal Industries 105 80 Health Services 103 30 Rubber and Misc Plastic Products 100 57 Home Furniture, Furnishing, and Equip 98 39 Miscellaneous Manufacturing 93 26 Paper and Allied Products 91 53 General Merchandise Stores 83 13 Oil and Gas Extraction 81 25 Furniture and Fixtures 79 54 Food Stores 67 22 Textile Mill Products 62 55 Auto Dealers and Gas Service Stations 59 32 Stone, Clay, Glass, and Concrete Products 55 87 Engnr, Acct, Rsrch, Mgmt and Related Svcs 47 24 Lumber and Wood Products 43 31 Leather and Leather Products 42 All

Others 196

TOTAL 6685

60

Table 2 Univariate Analysis

Sample firm years are drawn from 1987 through 2004. Three separate samples are drawn for each of the three earnings benchmarks (profit, zero change, and analysts’ forecasted earnings). Means, medians, and t-statistics from t-tests for the difference in means are reported. Panel A Firms that just miss or just meet/beat the profit benchmark. The sample is limited to firms with reported earnings `efore extraordilary items between –1.0% and 1.0% of total assets. The full sample is 3,434 firms.

Pre-SOX

Post-SOX

Just Miss

Just Beat Difference in Means

Just Miss

Just Beat

Difference in Means

Mean Median

Mean Median

t-stat (p-value)

Mean Median

Mean Median

t-stat (p-value)

CFO 2.77 1.08

3.48 2.46

2.47 (0.0135)

4.76 4.87

4.71 5.02

-0.05 (0.9597)

Abnormal Accruals 1.58

1.33 1.82 1.50

0.76 (0.4469)

0.89 0.48

2.02 1.28

1.22 (0.2227)

Abnormal Prod Cost 6.26

4.35 4.18 4.90

-1.27 (0.2027)

-2.36 -1.16

3.02 2.49

2.02 (0.0446)

Abnormal Disc Exp -5.12

-4.76 -4.50 -5.01

0.81 (0.4156)

-0.65 -1.69

-3.68 -4.39

-1.00 (0.3194)

N 1,084 2,056 115 179 * Significant at the 0.10 level (two-tail) **Significant at the 0.05 level (two-tail)

61

Panel B Firms that just miss or just meet/beat the earnings change benchmark. The sample is limited to firms with earnings changes between –1.0% and 1.0% of total assets. The full sample is 5,397 firms.

Pre-SOX

Post-SOX

Just Miss

Just Beat Difference in Means

Just Miss

Just Beat

Difference in Means

Mean Median

Mean Median

t-stat (p-value)

Mean Median

Mean Median

t-stat (p-value)

CFO 7.44 7.92

8.93 9.30

5.52 (<0.0001)

7.91 7.87

9.41 9.17

2.35 (0.0188)

Abnormal Accruals 0.95

0.64 1.01 0.76

0.32 (0.7503)

0.27 0.37

0.67 0.71

1.04 (0.2991)

Abnormal Prod Cost 1.02

2.08 0.46 1.06

-0.91 (0.3643)

0.45 0.44

1.91 3.10

1.74 (0.0829)

Abnormal Disc Exp -1.37

-2.95 -1.48 -2.34

-0.18 (0.8561)

-1.83 -3.46

-0.83 -2.82

0.68 (0.4983)

N 1,897 2,819 292 389 * Significant at the 0.10 level (two-tail) **Significant at the 0.05 level (two-tail)

62

Panel C Firms that just miss or just meet/beat the analysts’ forecasted earnings benchmark. The sample is limited to firms with actual earnings between –0.01 and 0.01 of the most recent analyst forecast of annual EPS. The full sample is 6,685 firms.

Pre-SOX

Post-SOX

Just Miss

Just Beat Difference in Means

Just Miss

Just Beat

Difference in Means

Mean Median

Mean Median

t-stat (p-value)

Mean Median

Mean Median

t-stat (p-value)

CFO 10.32 10.26

12.81 11.54

4.45 (<0.0001)

9.29 10.92

9.91 10.46

0.64 (0.5223)

Abnormal Accruals 0.95

0.79 0.98 0.72

0.15 (0.8841)

-0.14 -0.09

0.40 -0.14

1.22 (0.2228)

Abnormal Prod Cost -4.31

-2.76 -8.59 -4.32

-1.87 (0.0612)

-2.52 -1.57

-4.95 -2.84

-1.76 (0.0794)

Abnormal Disc Exp 3.51

0.07 4.66 0.62

1.25 (0.2095)

1.91 -0.37

4.91 0.72

1.85 (0.0645)

N 2,449 3,246 485 505 * Significant at the 0.10 level (two-tail) **Significant at the 0.05 level (two-tail) Variable Definitions CFO is cash flow from operations. AbAccr (abnormal accruals) is defined as the difference between total accruals and expected accruals. Expected accruals are calculated using the Jones model (Jones 1991). The Jones model estimates expected accruals as: Accruals t /A 1−t = α 0 + α 1 *(1/ A 1−t ) + β1 *(∆S t / A 1−t ) + β 2 *(PPE t /A 1−t ) + ε t . All terms are scaled by total assets at the end of year t-1. AbProd (abnormal production costs) is defined as the difference between a firm’s actual production costs and expected production costs. Expected production costs are estimated using the industry-year regression: Prod t = a 0 + a 1 *(1/A 1−t ) + b1 *Sales t + b 2 *∆Sales t + b 3 *∆Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1.

63

AbDisc (abnormal discretionary costs) is defined as the difference between a firm’s actual discretionary costs and expected discretionary costs. Expected discretionary costs are estimated using the industry-year regression: Disc t = a 0 + a 1 * (1/A 1−t ) + b1 * Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1.

64

Table 3 Comparison of Just Meet/Beat Firm-Years With Just Miss Firm

Years This table reports the results of probit analysis for firm-years located just to the right and left of the profit, earnings change, and analyst forecast benchmarks for years 1987 – 2004. BM = a + 1b AbAccr + 2b Abprod + 3b AbDisc + 4b SOX +

5b AbAccr x SOX + 6b Abprod x SOX + 7b AbDisc x SOX + 8b CFO + 9b NOA + E

* Significant at the 0.10 level (one-tail) **Significant at the 0.05 level (one-tail)

Profit Earnings Change Analyst Forecast

Predicted Sign

Estimate (Pr > χ 2 )

Estimate (Pr > χ 2 )

Estimate (Pr > χ 2 )

Intercept ? 0.2964 (<0.0001)**

0.2097 (<0.0001)**

0.1004 (<0.0001)**

AbAccr + 1.6090 (<0.0001)**

0.7542 (0.0104)**

0.4755 (0.0112)**

AbProd + -0.0313 (0.7160)

0.1248 (0.1724)

-0.0385 (0.9076)

AbDisc - 0.0703 (0.7207)

-0.1060 (0.2027)

0.0448 (0.7631)

SOX - -0.1819 (0.0118)**

-0.0629 (0.1156)

-0.1432 (0.0006)**

AbAccr*SOX - 1.3764 (0.9064)

1.2096 (0.8813)

0.6412 (0.8519)

AbProd*SOX + 0.8396 (0.0321)**

0.7965 (0.0477)**

0.1346 (0.3136)

AbDisc*SOX - 0.0542 (0.5544)

-0.3558 (0.7928)

0.3868 (0.9465)

CFO + 2.1111 (<0.0001)**

∆CFO + 1.0510 (<0.0001)**

0.5415 (<0.0001)**

NOA ? 0.2058 (0.0010)**

0.1425 (0.0009)**

0.0163 (0.5963)

N 3,434 5,397 6,685

65

Variable Definitions Profit Benchmark: BM = 1 where firm-years have scaled earnings

(NI t / TA 1−t ) greater than or equal to 0 but less than 0.01, and BM

= 0 where firm-years have scaled earnings greater than or equal to –0.01 but less than 0. Earnings Change Benchmark: BM = 1 where firm-years have scaled earnings changes (NI t - NI 1−t / TA 1−t ) greater than or equal to 0

but less than 0.01, and BM = 0 where firm-years have scaled earnings changes greater than or equal to –0.01 but less than 0. Analysts’ Forecast Benchmark: BM = 1 where firm-years have (EPS – forecasted EPS) greater than or equal to 0 but less that 0.01, and BM = 0 where firm-years have (EPS – forecasted EPS) greater than or equal to –0.01 but less than 0. Forecasted EPS is defined as the most recent analyst forecast prior to the announcement of annual earnings. Prod (production costs) is defined as Costs of goods sold + Change in inventory. Disc (discretionary expenses) is defined as Selling, General, and Administrative expenses + R&D + Advertising expenses. AbAccr (abnormal accruals) is defined as the difference between total accruals and expected accruals. Expected accruals are calculated using the Jones model (Jones 1991). The Jones model estimates expected accruals as: Accruals t /A 1−t = α 0 + α 1 *(1/ A 1−t ) + β1 *(∆S t / A 1−t ) + β 2 *(PPE t /A 1−t ) + ε t . All terms are scaled by total assets at the end of year t-1. AbProd (abnormal production costs) is defined as the difference between a firm’s actual production costs and expected production costs. Expected production costs are estimated using the industry-year regression: Prod t = a 0 + a 1 *(1/A 1−t ) + b1 *Sales t + b 2 *∆Sales t + b 3 *∆Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1. AbDisc (abnormal discretionary costs) is defined as the difference between a firm’s actual discretionary costs and expected discretionary costs. Expected discretionary costs are estimated using the industry-year regression: Disc t = a 0 + a1 * (1/A 1−t ) + b 1 * Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1.

66

SOX is an indicator variable taking on the value of 1 if the firm year is 2003 or 2004 (years following the adoption of Sarbanes – Oxley). SOX = 0 for years prior to 2002. CFO is cash flow from operations. ∆CFO is the change is cash flow from operations from year t-1 to year t. NOA is net operating assets defines as total shareholder’s equity – cash and short term investments + total debt.

67

Table 4 Comparison of Real Earnings Management Levels Between Suspect

Firms and Non-Suspect Firms Panel A Comparison of abnormal production cost levels between suspect firms and non-suspect firms. Suspect firms are defined as firms with reported earnings before extraordinary items between –1.0% and 1.0% of total assets. The sample is limited to firms with reporting earnings before extraordinary items between –7.5% and 7.5% of total assets. OLS regression is used for the following equation: Abprod = a + 1b Suspect + 2b AbMTB + 3b AbSize + 4b AbNI + E

N = 18,546 Coefficient t-Stat p-value Intercept 0.0245 10.27 <.0001** Suspect 0.0213 2.53 0.0115** AbMTB -0.0002 -2.82 0.0018** AbSize 0.0068 5.81 <.0001** AbNI -0.0029 -5.19 <.0001** * Significant at the 0.10 level **Significant at the 0.05 level (two-tail)

Panel B Comparison of abnormal discretionary expense levels between suspect firms and non-suspect firms. Suspect firms are defined as firms with reported earnings before extraordinary items between –1.0% and 1.0% of total assets. The sample is limited to firms with reporting earnings before extraordinary items between –7.5% and 7.5% of total assets. OLS regression is used for the following equation: AbDisc = a + 1b Suspect + 2b AbMTB + 3b AbSize + 4b AbNI + E

N = 18,546 Coefficient t-Stat p-value Intercept -0.0597 -33.19 <.0001** Suspect -0.2557 -3.69 0.0002** AbMTB -0.0001 -0.21 0.8319 AbSize 0.0066 6.87 <.0001** AbNI -0.0006 6.26 <.0001** * Significant at the 0.10 level **Significant at the 0.05 level (two-tail)

68

Variable Definitions AbProd (abnormal production costs) is defined as the difference between a firm’s actual production costs and expected production costs. Expected production costs are estimated using the industry-year regression: Prod t = a 0 + a 1 *(1/A 1−t ) + b1 *Sales t + b 2 *∆Sales t + b 3 *∆Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1. AbDisc (abnormal discretionary costs) is defined as the difference between a firm’s actual discretionary costs and expected discretionary costs. Expected discretionary costs are estimated using the industry-year regression: Disc t = a 0 + a1 * (1/A 1−t ) + b 1 * Sales 1−t + ε t . All terms are scaled by total assets at the end of year t-1. Suspect is an indicator variable taking on the value of 1 if the firm-year observations has scaled earnings (EBEI t / TA 1−t ) greater

than or equal to 0 but less than 0.01. AbMTB (abnormal market-to-book) is the firm MTB subtracted from the industry mean MTB. All terms are scaled by total assets at the end of year t-1. AbSize (abnormal size) is the logarithm of market value of equity subtracted from the industry mean logarithm of market value of equity. All terms are scaled by total assets at the end of year t-1. AbNI (abnormal net income) is the scaled income before extraordinary items subtracted from the industry mean scaled income before extraordinary items. All terms are scaled by total assets at the end of year t-1.

69

Table 5 Time Trends in Accrual Management

This table reports results of a time-trend analysis of accrual management from 1987 through 2004. Results show an increase in accrual management from 1987 until the passage of Sarbanes-Oxley, and a sharp decline in accrual management in the years following Sarbanes-Oxley. The sample is 43,786 firm years drawn from the entire distribution of earnings. OLS regression is used on the following model: AbAccr = a + 1b TIME + 2b SOX + E

N = 43,786 Coefficient t-stat p-value Intercept -0.0163 -5.42 <.0001** TIME 0.0022 6.75 <.0001** SOX -0.0139 -3.08 0.0021** * Significant at the 0.10 level **Significant at the 0.05 level (two-tail) Variable Definitions AbAccr (abnormal accruals) is defined as the difference between total accruals and expected accruals. Expected accruals are calculated using the Jones model (Jones 1991). The Jones model estimates expected accruals as: Accruals t /A 1−t = α 0 + α 1 *(1/ A 1−t ) + β1 *(∆S t / A 1−t ) + β 2 *(PPE t /A 1−t ) + ε t . TIME is a time trend variable measured as the years away from 1987. For instance, for year 1989 TIME would equal 2 (1989 – 1987). SOX is an indicator variable taking on the value of 1 if the firm year is 2003 or 2004 (years following the adoption of Sarbanes – Oxley). SOX = 0 for years prior to 2002.